Genetically-Based Active Flow Control And PIV Measurements Of A Circular Cylinder Wake

Abstract

In the field of flow manipulation, considerable attention is devoted to controlling the wake of bluff bodies, which are characterized by the phenomenon of vortex shedding. Specifically, the control of the wake behind a single circular cylinder has garnered substantial research interest due to its engineering applications, such as reducing aerodynamic drag in the automotive and aerospace industries. Therefore, in the current work, through the application of the planar PIV technique, the fluid dynamic behavior of a circular cylinder wake controlled via synthetic jets (SJ) and Machine Learning (ML) methods is analyzed and discussed. ML algorithms are used to determine the optimal voltage signal for the loudspeaker, which effectively minimizes the aerodynamic drag of the cylinder. This approach overcomes the limitations associated with the a priori assumption of a parametric waveshape. In particular, the gradient-enriched machine learning control (gMLC) algorithm is chosen as optimization tool to search the best open-loop control policy for aerodynamic drag reduction. The cost function for this problem has been defined considering both the drag reduction contribution and the electrical power spent for the actuation. Initial optimization tests are performed by minimizing a cost function considering only the reduction in aerodynamic drag. Subsequently, a term related to the electrical power consumption for actuation is incorporated into the cost function. Once obtained the optimal control policies for these cases (hereinafter renamed CC1 and CC2, respectively), the physical mechanism responsible for reducing the aerodynamic drag is investigated more closely by analyzing the effect of the control laws on the turbulent flow statistics. Figure 1 reports a comparative assessment of the CC1 and CC2 with the baseline configuration and the configuration controlled by a sinusoidal law (CCS). The baseline configuration is characterized by an extended region of localized turbulent fluctuations in the far wake, where the time-averaged signature of von Kármán vortices is found. In contrast, all the controlled configurations show a global reduction in wake turbulence, with a peak of turbulent energy at the rear stagnation point due to the momentum injection from the SJ actuator. Notably, the CCS and CC2 configurations share similar flow field morphologies, while the CC1 configuration shows a greater reduction in turbulent fluctuations compared to the other controlled cases. This control law also achieves the largest drag reduction among all configurations studied reporting a 9.7% of drag reduction, compared to CCS and CC2, which exhibit similar reductions of 8.4% and 8.5%, respectively. Therefore, the optimization procedure has proven the capability of finding control policies able to outperform the traditional sinusoidal control widely studied in literature.